Multiomic Network Analysis Identifies Dysregulated Neurobiological Pathways in Opioid Addiction
Kyle A Sullivan1, David Kainer1, Matthew Lane2
1Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee.
Background:
Opioid addiction is a worldwide public health crisis. In the United States, for example, opioids cause more drug overdose deaths than any other substance. However, opioid addiction treatments have limited efficacy, meaning that additional treatments are needed.
Methods:
To help address this problem, we used network-based machine learning techniques to integrate results from genome-wide association studies of opioid use disorder and problematic prescription opioid misuse with transcriptomic, proteomic, and epigenetic data from the dorsolateral prefrontal cortex of people who died of opioid overdose and control individuals.
Results:
We identified 211 highly interrelated genes identified by genome-wide association studies or dysregulation in the dorsolateral prefrontal cortex of people who died of opioid overdose that implicated the Akt, BDNF (brain-derived neurotrophic factor), and ERK (extracellular signal-regulated kinase) pathways, identifying 414 drugs targeting 48 of these opioid addiction-associated genes. Some of the identified drugs are approved to treat other substance use disorders or depression.
Conclusions:
Our synthesis of multiomics using a systems biology approach revealed key gene targets that could contribute to drug repurposing, genetics-informed addiction treatment, and future discovery.
Insights
Opioid addiction treatments need improvement. This study identified key genes and drugs for new addiction therapies by analyzing brain data from overdose victims using machine learning.
Area of Science:
- Neuroscience
- Genetics
- Pharmacology
Background:
- Opioid addiction is a global health crisis, particularly in the US, with significant overdose deaths.
- Current opioid addiction treatments demonstrate limited efficacy, necessitating the development of novel therapeutic strategies.
Purpose of the Study:
- To identify novel therapeutic targets for opioid addiction.
- To explore drug repurposing opportunities for treating opioid use disorder.
Main Methods:
- Utilized network-based machine learning to integrate genome-wide association studies (GWAS) with multi-omics data (transcriptomic, proteomic, epigenetic).
- Analyzed dorsolateral prefrontal cortex data from individuals who died of opioid overdose and control subjects.
Main Results:
- Identified 211 interrelated genes associated with opioid addiction, implicating Akt, BDNF, and ERK pathways.
- Discovered 414 potential drugs targeting 48 of these addiction-associated genes.
- Some identified drugs are already approved for treating depression or other substance use disorders.
Conclusions:
- A systems biology approach integrating multi-omics data revealed crucial gene targets for opioid addiction.
- Findings support drug repurposing and the development of genetics-informed treatments for addiction.
- This research opens avenues for future discoveries in addiction neuroscience and pharmacology.
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